{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c464e9e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#导包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.family']=['SimHei']#显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False#用来显示正常正负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bdeb51b8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品牌ID</th>\n",
       "      <th>品牌名称</th>\n",
       "      <th>城市</th>\n",
       "      <th>平台</th>\n",
       "      <th>日期</th>\n",
       "      <th>门店ID</th>\n",
       "      <th>门店名称</th>\n",
       "      <th>GMV</th>\n",
       "      <th>下单人数</th>\n",
       "      <th>商家实收</th>\n",
       "      <th>商户补贴</th>\n",
       "      <th>平台补贴</th>\n",
       "      <th>无效订单数</th>\n",
       "      <th>曝光人数</th>\n",
       "      <th>有效订单数</th>\n",
       "      <th>进店人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4636</td>\n",
       "      <td>蛙小辣火锅杯（总账号）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2019-08-21</td>\n",
       "      <td>8165842</td>\n",
       "      <td>虹口足球场店</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4636</td>\n",
       "      <td>蛙小辣火锅杯（总账号）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2019-08-21</td>\n",
       "      <td>8184590</td>\n",
       "      <td>五角场店</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4636</td>\n",
       "      <td>蛙小辣火锅杯（总账号）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2019-08-21</td>\n",
       "      <td>8106681</td>\n",
       "      <td>怒江路店</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4636</td>\n",
       "      <td>蛙小辣火锅杯（总账号）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2019-08-22</td>\n",
       "      <td>8165842</td>\n",
       "      <td>虹口足球场店</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4636</td>\n",
       "      <td>蛙小辣火锅杯（总账号）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2019-08-22</td>\n",
       "      <td>8184590</td>\n",
       "      <td>五角场店</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2380</th>\n",
       "      <td>6108</td>\n",
       "      <td>拌客（武宁路店）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2020-08-13</td>\n",
       "      <td>9698624</td>\n",
       "      <td>拌客干拌麻辣烫(武宁路店)</td>\n",
       "      <td>3877</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1396</td>\n",
       "      <td>2087</td>\n",
       "      <td>314</td>\n",
       "      <td>3</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>79</td>\n",
       "      <td>452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2381</th>\n",
       "      <td>6108</td>\n",
       "      <td>拌客（武宁路店）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>9698624</td>\n",
       "      <td>拌客干拌麻辣烫(武宁路店)</td>\n",
       "      <td>4386</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1598</td>\n",
       "      <td>2343</td>\n",
       "      <td>397</td>\n",
       "      <td>3</td>\n",
       "      <td>5588.0</td>\n",
       "      <td>87</td>\n",
       "      <td>411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2382</th>\n",
       "      <td>6108</td>\n",
       "      <td>拌客（武宁路店）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2020-08-12</td>\n",
       "      <td>9698624</td>\n",
       "      <td>拌客干拌麻辣烫(武宁路店)</td>\n",
       "      <td>4628</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1738</td>\n",
       "      <td>2416</td>\n",
       "      <td>430</td>\n",
       "      <td>3</td>\n",
       "      <td>7411.0</td>\n",
       "      <td>97</td>\n",
       "      <td>528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2383</th>\n",
       "      <td>6108</td>\n",
       "      <td>拌客（武宁路店）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2020-08-19</td>\n",
       "      <td>9698624</td>\n",
       "      <td>拌客干拌麻辣烫(武宁路店)</td>\n",
       "      <td>5410</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1737</td>\n",
       "      <td>3122</td>\n",
       "      <td>166</td>\n",
       "      <td>4</td>\n",
       "      <td>7234.0</td>\n",
       "      <td>113</td>\n",
       "      <td>551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2384</th>\n",
       "      <td>6108</td>\n",
       "      <td>拌客（武宁路店）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2020-08-18</td>\n",
       "      <td>9698624</td>\n",
       "      <td>拌客干拌麻辣烫(武宁路店)</td>\n",
       "      <td>5350</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1913</td>\n",
       "      <td>2906</td>\n",
       "      <td>477</td>\n",
       "      <td>5</td>\n",
       "      <td>6005.0</td>\n",
       "      <td>104</td>\n",
       "      <td>501.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2385 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      品牌ID         品牌名称  城市       平台          日期     门店ID           门店名称  \\\n",
       "0     4636  蛙小辣火锅杯（总账号）  上海  meituan  2019-08-21  8165842         虹口足球场店   \n",
       "1     4636  蛙小辣火锅杯（总账号）  上海  meituan  2019-08-21  8184590           五角场店   \n",
       "2     4636  蛙小辣火锅杯（总账号）  上海  meituan  2019-08-21  8106681           怒江路店   \n",
       "3     4636  蛙小辣火锅杯（总账号）  上海  meituan  2019-08-22  8165842         虹口足球场店   \n",
       "4     4636  蛙小辣火锅杯（总账号）  上海  meituan  2019-08-22  8184590           五角场店   \n",
       "...    ...          ...  ..      ...         ...      ...            ...   \n",
       "2380  6108     拌客（武宁路店）  上海  meituan  2020-08-13  9698624  拌客干拌麻辣烫(武宁路店)   \n",
       "2381  6108     拌客（武宁路店）  上海  meituan  2020-08-17  9698624  拌客干拌麻辣烫(武宁路店)   \n",
       "2382  6108     拌客（武宁路店）  上海  meituan  2020-08-12  9698624  拌客干拌麻辣烫(武宁路店)   \n",
       "2383  6108     拌客（武宁路店）  上海  meituan  2020-08-19  9698624  拌客干拌麻辣烫(武宁路店)   \n",
       "2384  6108     拌客（武宁路店）  上海  meituan  2020-08-18  9698624  拌客干拌麻辣烫(武宁路店)   \n",
       "\n",
       "       GMV   下单人数  商家实收  商户补贴  平台补贴  无效订单数    曝光人数  有效订单数   进店人数  \n",
       "0        0    NaN     0     0     0      0     NaN      0    NaN  \n",
       "1        0    NaN     0     0     0      0     NaN      0    NaN  \n",
       "2        0    NaN     0     0     0      0     NaN      0    NaN  \n",
       "3        0    NaN     0     0     0      0     NaN      0    NaN  \n",
       "4        0    NaN     0     0     0      0     NaN      0    NaN  \n",
       "...    ...    ...   ...   ...   ...    ...     ...    ...    ...  \n",
       "2380  3877   79.0  1396  2087   314      3  6012.0     79  452.0  \n",
       "2381  4386   86.0  1598  2343   397      3  5588.0     87  411.0  \n",
       "2382  4628   99.0  1738  2416   430      3  7411.0     97  528.0  \n",
       "2383  5410  113.0  1737  3122   166      4  7234.0    113  551.0  \n",
       "2384  5350  104.0  1913  2906   477      5  6005.0    104  501.0  \n",
       "\n",
       "[2385 rows x 16 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('shop.csv',encoding='gbk')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a241262",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2385 entries, 0 to 2384\n",
      "Data columns (total 16 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   品牌ID    2385 non-null   int64  \n",
      " 1   品牌名称    2385 non-null   object \n",
      " 2   城市      2385 non-null   object \n",
      " 3   平台      2385 non-null   object \n",
      " 4   日期      2385 non-null   object \n",
      " 5   门店ID    2385 non-null   int64  \n",
      " 6   门店名称    2385 non-null   object \n",
      " 7   GMV     2385 non-null   int64  \n",
      " 8   下单人数    1550 non-null   float64\n",
      " 9   商家实收    2385 non-null   int64  \n",
      " 10  商户补贴    2385 non-null   int64  \n",
      " 11  平台补贴    2385 non-null   int64  \n",
      " 12  无效订单数   2385 non-null   int64  \n",
      " 13  曝光人数    1550 non-null   float64\n",
      " 14  有效订单数   2385 non-null   int64  \n",
      " 15  进店人数    1550 non-null   float64\n",
      "dtypes: float64(3), int64(8), object(5)\n",
      "memory usage: 298.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96580cc5",
   "metadata": {},
   "source": [
    "# 缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30330256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "品牌ID       0\n",
       "品牌名称       0\n",
       "城市         0\n",
       "平台         0\n",
       "日期         0\n",
       "门店ID       0\n",
       "门店名称       0\n",
       "GMV        0\n",
       "下单人数     835\n",
       "商家实收       0\n",
       "商户补贴       0\n",
       "平台补贴       0\n",
       "无效订单数      0\n",
       "曝光人数     835\n",
       "有效订单数      0\n",
       "进店人数     835\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ab1a9dd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno#查看合并表data1的缺失值情况\n",
    "missingno.matrix(df,figsize=(30,5)) #以矩阵的形式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "273f0905",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将所有列中值为 0 的数据替换为 NaN\n",
    "df.replace(0, np.nan, inplace=True)\n",
    "df['日期'] = pd.to_datetime(df['日期'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "841e385f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "品牌ID        0\n",
       "品牌名称        0\n",
       "城市          0\n",
       "平台          0\n",
       "日期          0\n",
       "门店ID        0\n",
       "门店名称        0\n",
       "GMV      1002\n",
       "下单人数     1005\n",
       "商家实收     1002\n",
       "商户补贴     1002\n",
       "平台补贴     1007\n",
       "无效订单数    1986\n",
       "曝光人数      840\n",
       "有效订单数    1002\n",
       "进店人数      842\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4861311c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品牌ID</th>\n",
       "      <th>品牌名称</th>\n",
       "      <th>城市</th>\n",
       "      <th>平台</th>\n",
       "      <th>日期</th>\n",
       "      <th>门店ID</th>\n",
       "      <th>门店名称</th>\n",
       "      <th>GMV</th>\n",
       "      <th>下单人数</th>\n",
       "      <th>商家实收</th>\n",
       "      <th>商户补贴</th>\n",
       "      <th>平台补贴</th>\n",
       "      <th>无效订单数</th>\n",
       "      <th>曝光人数</th>\n",
       "      <th>有效订单数</th>\n",
       "      <th>进店人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>4636</td>\n",
       "      <td>蛙小辣火锅杯（总账号）</td>\n",
       "      <td>上海</td>\n",
       "      <td>eleme</td>\n",
       "      <td>2019-10-10</td>\n",
       "      <td>2000555792</td>\n",
       "      <td>虹口足球场店</td>\n",
       "      <td>1144.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>191.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>4636</td>\n",
       "      <td>蛙小辣火锅杯（总账号）</td>\n",
       "      <td>上海</td>\n",
       "      <td>eleme</td>\n",
       "      <td>2019-10-10</td>\n",
       "      <td>2000507076</td>\n",
       "      <td>五角场店</td>\n",
       "      <td>319.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>4636</td>\n",
       "      <td>蛙小辣火锅杯（总账号）</td>\n",
       "      <td>上海</td>\n",
       "      <td>eleme</td>\n",
       "      <td>2019-10-11</td>\n",
       "      <td>2000555792</td>\n",
       "      <td>虹口足球场店</td>\n",
       "      <td>754.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>4636</td>\n",
       "      <td>蛙小辣火锅杯（总账号）</td>\n",
       "      <td>上海</td>\n",
       "      <td>eleme</td>\n",
       "      <td>2019-10-11</td>\n",
       "      <td>2000507076</td>\n",
       "      <td>五角场店</td>\n",
       "      <td>885.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>467.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2934.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>4636</td>\n",
       "      <td>蛙小辣火锅杯（总账号）</td>\n",
       "      <td>上海</td>\n",
       "      <td>eleme</td>\n",
       "      <td>2019-10-12</td>\n",
       "      <td>2000555792</td>\n",
       "      <td>虹口足球场店</td>\n",
       "      <td>1562.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3171.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2380</th>\n",
       "      <td>6108</td>\n",
       "      <td>拌客（武宁路店）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2020-08-13</td>\n",
       "      <td>9698624</td>\n",
       "      <td>拌客干拌麻辣烫(武宁路店)</td>\n",
       "      <td>3877.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1396.0</td>\n",
       "      <td>2087.0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2381</th>\n",
       "      <td>6108</td>\n",
       "      <td>拌客（武宁路店）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>9698624</td>\n",
       "      <td>拌客干拌麻辣烫(武宁路店)</td>\n",
       "      <td>4386.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1598.0</td>\n",
       "      <td>2343.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5588.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2382</th>\n",
       "      <td>6108</td>\n",
       "      <td>拌客（武宁路店）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2020-08-12</td>\n",
       "      <td>9698624</td>\n",
       "      <td>拌客干拌麻辣烫(武宁路店)</td>\n",
       "      <td>4628.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1738.0</td>\n",
       "      <td>2416.0</td>\n",
       "      <td>430.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7411.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2383</th>\n",
       "      <td>6108</td>\n",
       "      <td>拌客（武宁路店）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2020-08-19</td>\n",
       "      <td>9698624</td>\n",
       "      <td>拌客干拌麻辣烫(武宁路店)</td>\n",
       "      <td>5410.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1737.0</td>\n",
       "      <td>3122.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7234.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2384</th>\n",
       "      <td>6108</td>\n",
       "      <td>拌客（武宁路店）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2020-08-18</td>\n",
       "      <td>9698624</td>\n",
       "      <td>拌客干拌麻辣烫(武宁路店)</td>\n",
       "      <td>5350.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>2906.0</td>\n",
       "      <td>477.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6005.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>501.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1384 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      品牌ID         品牌名称  城市       平台         日期        门店ID           门店名称  \\\n",
       "250   4636  蛙小辣火锅杯（总账号）  上海    eleme 2019-10-10  2000555792         虹口足球场店   \n",
       "251   4636  蛙小辣火锅杯（总账号）  上海    eleme 2019-10-10  2000507076           五角场店   \n",
       "258   4636  蛙小辣火锅杯（总账号）  上海    eleme 2019-10-11  2000555792         虹口足球场店   \n",
       "259   4636  蛙小辣火锅杯（总账号）  上海    eleme 2019-10-11  2000507076           五角场店   \n",
       "266   4636  蛙小辣火锅杯（总账号）  上海    eleme 2019-10-12  2000555792         虹口足球场店   \n",
       "...    ...          ...  ..      ...        ...         ...            ...   \n",
       "2380  6108     拌客（武宁路店）  上海  meituan 2020-08-13     9698624  拌客干拌麻辣烫(武宁路店)   \n",
       "2381  6108     拌客（武宁路店）  上海  meituan 2020-08-17     9698624  拌客干拌麻辣烫(武宁路店)   \n",
       "2382  6108     拌客（武宁路店）  上海  meituan 2020-08-12     9698624  拌客干拌麻辣烫(武宁路店)   \n",
       "2383  6108     拌客（武宁路店）  上海  meituan 2020-08-19     9698624  拌客干拌麻辣烫(武宁路店)   \n",
       "2384  6108     拌客（武宁路店）  上海  meituan 2020-08-18     9698624  拌客干拌麻辣烫(武宁路店)   \n",
       "\n",
       "         GMV   下单人数    商家实收    商户补贴   平台补贴  无效订单数    曝光人数  有效订单数   进店人数  \n",
       "250   1144.0   18.0   441.0   576.0   54.0    NaN   191.0   18.0   53.0  \n",
       "251    319.0    7.0   159.0   135.0   17.0    1.0   255.0    7.0   56.0  \n",
       "258    754.0   13.0   303.0   371.0   54.0    1.0   829.0   13.0   80.0  \n",
       "259    885.0   16.0   330.0   467.0   63.0    NaN  2934.0   17.0  236.0  \n",
       "266   1562.0   23.0   669.0   722.0  137.0    NaN  3171.0   25.0  261.0  \n",
       "...      ...    ...     ...     ...    ...    ...     ...    ...    ...  \n",
       "2380  3877.0   79.0  1396.0  2087.0  314.0    3.0  6012.0   79.0  452.0  \n",
       "2381  4386.0   86.0  1598.0  2343.0  397.0    3.0  5588.0   87.0  411.0  \n",
       "2382  4628.0   99.0  1738.0  2416.0  430.0    3.0  7411.0   97.0  528.0  \n",
       "2383  5410.0  113.0  1737.0  3122.0  166.0    4.0  7234.0  113.0  551.0  \n",
       "2384  5350.0  104.0  1913.0  2906.0  477.0    5.0  6005.0  104.0  501.0  \n",
       "\n",
       "[1384 rows x 16 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.dropna(thresh=10)#删除一行中不存在10个及10个以上的存在值的行\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16cea8de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "品牌ID       0\n",
       "品牌名称       0\n",
       "城市         0\n",
       "平台         0\n",
       "日期         0\n",
       "门店ID       0\n",
       "门店名称       0\n",
       "GMV        1\n",
       "下单人数       4\n",
       "商家实收       1\n",
       "商户补贴       1\n",
       "平台补贴       6\n",
       "无效订单数    985\n",
       "曝光人数       4\n",
       "有效订单数      1\n",
       "进店人数       4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a321e91d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\temp\\ipykernel_17888\\370121109.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[col].fillna(mean_value, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "num_columns = ['GMV', '下单人数', '商家实收', '商户补贴', '平台补贴', '无效订单数', '曝光人数', '有效订单数', '进店人数']\n",
    "# 对每个列分别计算均值并填充空值\n",
    "for col in num_columns:\n",
    "    mean_value = df[col].mean()\n",
    "    df[col].fillna(mean_value, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "66560779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "品牌ID     0\n",
       "品牌名称     0\n",
       "城市       0\n",
       "平台       0\n",
       "日期       0\n",
       "门店ID     0\n",
       "门店名称     0\n",
       "GMV      0\n",
       "下单人数     0\n",
       "商家实收     0\n",
       "商户补贴     0\n",
       "平台补贴     0\n",
       "无效订单数    0\n",
       "曝光人数     0\n",
       "有效订单数    0\n",
       "进店人数     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aaae7d22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1384 entries, 250 to 2384\n",
      "Data columns (total 16 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   品牌ID    1384 non-null   int64         \n",
      " 1   品牌名称    1384 non-null   object        \n",
      " 2   城市      1384 non-null   object        \n",
      " 3   平台      1384 non-null   object        \n",
      " 4   日期      1384 non-null   datetime64[ns]\n",
      " 5   门店ID    1384 non-null   int64         \n",
      " 6   门店名称    1384 non-null   object        \n",
      " 7   GMV     1384 non-null   float64       \n",
      " 8   下单人数    1384 non-null   float64       \n",
      " 9   商家实收    1384 non-null   float64       \n",
      " 10  商户补贴    1384 non-null   float64       \n",
      " 11  平台补贴    1384 non-null   float64       \n",
      " 12  无效订单数   1384 non-null   float64       \n",
      " 13  曝光人数    1384 non-null   float64       \n",
      " 14  有效订单数   1384 non-null   float64       \n",
      " 15  进店人数    1384 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(9), int64(2), object(4)\n",
      "memory usage: 183.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "df2ceebc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除完全相同的重复行\n",
    "df = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "90136b37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "250     2000555792\n",
      "251     2000507076\n",
      "258     2000555792\n",
      "259     2000507076\n",
      "266     2000555792\n",
      "           ...    \n",
      "2380       9698624\n",
      "2381       9698624\n",
      "2382       9698624\n",
      "2383       9698624\n",
      "2384       9698624\n",
      "Name: 门店ID, Length: 1384, dtype: int64\n",
      "重复值个数为: 0\n"
     ]
    }
   ],
   "source": [
    "duplicates=df['门店ID'][df['门店ID'].duplicated(keep=False)]\n",
    "print(duplicates)\n",
    "print(\"重复值个数为:\",df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "47f3e615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品牌ID</th>\n",
       "      <th>日期</th>\n",
       "      <th>门店ID</th>\n",
       "      <th>GMV</th>\n",
       "      <th>下单人数</th>\n",
       "      <th>商家实收</th>\n",
       "      <th>商户补贴</th>\n",
       "      <th>平台补贴</th>\n",
       "      <th>无效订单数</th>\n",
       "      <th>曝光人数</th>\n",
       "      <th>有效订单数</th>\n",
       "      <th>进店人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1384.000000</td>\n",
       "      <td>1384</td>\n",
       "      <td>1.384000e+03</td>\n",
       "      <td>1384.000000</td>\n",
       "      <td>1384.000000</td>\n",
       "      <td>1384.000000</td>\n",
       "      <td>1384.000000</td>\n",
       "      <td>1384.000000</td>\n",
       "      <td>1384.000000</td>\n",
       "      <td>1384.000000</td>\n",
       "      <td>1384.000000</td>\n",
       "      <td>1384.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4834.890173</td>\n",
       "      <td>2020-03-21 08:39:11.445086720</td>\n",
       "      <td>1.044872e+09</td>\n",
       "      <td>2071.822126</td>\n",
       "      <td>35.829710</td>\n",
       "      <td>746.499638</td>\n",
       "      <td>1098.680405</td>\n",
       "      <td>126.346880</td>\n",
       "      <td>1.679198</td>\n",
       "      <td>2339.719565</td>\n",
       "      <td>36.856833</td>\n",
       "      <td>186.063043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4636.000000</td>\n",
       "      <td>2019-10-10 00:00:00</td>\n",
       "      <td>8.052557e+06</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4636.000000</td>\n",
       "      <td>2019-12-15 00:00:00</td>\n",
       "      <td>8.491999e+06</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>327.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.679198</td>\n",
       "      <td>963.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4636.000000</td>\n",
       "      <td>2020-03-22 00:00:00</td>\n",
       "      <td>2.000507e+09</td>\n",
       "      <td>1372.500000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>503.500000</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>1.679198</td>\n",
       "      <td>1527.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>113.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4636.000000</td>\n",
       "      <td>2020-06-16 06:00:00</td>\n",
       "      <td>2.001054e+09</td>\n",
       "      <td>3068.750000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>1080.250000</td>\n",
       "      <td>1655.500000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>1.679198</td>\n",
       "      <td>3401.500000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>294.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6108.000000</td>\n",
       "      <td>2020-09-25 00:00:00</td>\n",
       "      <td>2.043565e+09</td>\n",
       "      <td>11013.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>3780.000000</td>\n",
       "      <td>5882.000000</td>\n",
       "      <td>1141.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10621.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>833.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>503.380692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.731799e+08</td>\n",
       "      <td>1660.680943</td>\n",
       "      <td>31.532753</td>\n",
       "      <td>583.321355</td>\n",
       "      <td>905.539880</td>\n",
       "      <td>164.875452</td>\n",
       "      <td>0.688159</td>\n",
       "      <td>1849.209556</td>\n",
       "      <td>32.521862</td>\n",
       "      <td>149.586326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              品牌ID                             日期          门店ID           GMV  \\\n",
       "count  1384.000000                           1384  1.384000e+03   1384.000000   \n",
       "mean   4834.890173  2020-03-21 08:39:11.445086720  1.044872e+09   2071.822126   \n",
       "min    4636.000000            2019-10-10 00:00:00  8.052557e+06     63.000000   \n",
       "25%    4636.000000            2019-12-15 00:00:00  8.491999e+06    871.000000   \n",
       "50%    4636.000000            2020-03-22 00:00:00  2.000507e+09   1372.500000   \n",
       "75%    4636.000000            2020-06-16 06:00:00  2.001054e+09   3068.750000   \n",
       "max    6108.000000            2020-09-25 00:00:00  2.043565e+09  11013.000000   \n",
       "std     503.380692                            NaN  9.731799e+08   1660.680943   \n",
       "\n",
       "              下单人数         商家实收         商户补贴         平台补贴        无效订单数  \\\n",
       "count  1384.000000  1384.000000  1384.000000  1384.000000  1384.000000   \n",
       "mean     35.829710   746.499638  1098.680405   126.346880     1.679198   \n",
       "min       1.000000    23.000000    35.000000     2.000000     1.000000   \n",
       "25%      14.000000   327.000000   449.000000    27.000000     1.679198   \n",
       "50%      22.000000   503.500000   713.000000    58.000000     1.679198   \n",
       "75%      54.000000  1080.250000  1655.500000   164.000000     1.679198   \n",
       "max     224.000000  3780.000000  5882.000000  1141.000000    12.000000   \n",
       "std      31.532753   583.321355   905.539880   164.875452     0.688159   \n",
       "\n",
       "               曝光人数        有效订单数         进店人数  \n",
       "count   1384.000000  1384.000000  1384.000000  \n",
       "mean    2339.719565    36.856833   186.063043  \n",
       "min       85.000000     1.000000    14.000000  \n",
       "25%      963.000000    14.000000    71.000000  \n",
       "50%     1527.000000    22.000000   113.000000  \n",
       "75%     3401.500000    56.000000   294.000000  \n",
       "max    10621.000000   232.000000   833.000000  \n",
       "std     1849.209556    32.521862   149.586326  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()#描述性分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e6cab75",
   "metadata": {},
   "source": [
    "# 异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8e29c8af",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "D:\\anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "D:\\anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "D:\\anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "D:\\anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "D:\\anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "D:\\anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "D:\\anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "D:\\anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = ['GMV', '下单人数', '商家实收', '商户补贴', '平台补贴', '无效订单数', '曝光人数', '有效订单数', '进店人数']\n",
    "data = df[columns]\n",
    "\n",
    "# 计算四分位数和四分位距\n",
    "q1 = np.percentile(data, 25, axis=0)\n",
    "q3 = np.percentile(data, 75, axis=0)\n",
    "iqr = q3 - q1\n",
    "\n",
    "# 定义异常值的边界\n",
    "lower_bound = q1 - 1.5 * iqr\n",
    "upper_bound = q3 + 1.5 * iqr\n",
    "# 删除异常值\n",
    "df1=df[((data < lower_bound) | (data > upper_bound)).any(axis=1)]\n",
    "df = df[~((data < lower_bound) | (data > upper_bound)).any(axis=1)]\n",
    "\n",
    "# 绘制箱线图\n",
    "plt.figure(figsize=(10, 6))  # 设置图表大小\n",
    "sns.boxplot(data=data, orient='h', palette=\"pastel\")  # 使用 seaborn 绘制水平箱线图，并设置颜色\n",
    "plt.title('Boxplot', fontsize=15)  # 设置标题和字体大小\n",
    "plt.xlabel('Value', fontsize=12)  # 设置 x 轴标签和字体大小\n",
    "plt.yticks(fontsize=10)  # 设置 y 轴标签的字体大小\n",
    "plt.grid(axis='x', linestyle='--', alpha=0.7)  # 在 x 轴上添加网格线\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "85b68164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品牌ID</th>\n",
       "      <th>品牌名称</th>\n",
       "      <th>城市</th>\n",
       "      <th>平台</th>\n",
       "      <th>日期</th>\n",
       "      <th>门店ID</th>\n",
       "      <th>门店名称</th>\n",
       "      <th>GMV</th>\n",
       "      <th>下单人数</th>\n",
       "      <th>商家实收</th>\n",
       "      <th>商户补贴</th>\n",
       "      <th>平台补贴</th>\n",
       "      <th>无效订单数</th>\n",
       "      <th>曝光人数</th>\n",
       "      <th>有效订单数</th>\n",
       "      <th>进店人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>4636</td>\n",
       "      <td>蛙小辣火锅杯（总账号）</td>\n",
       "      <td>上海</td>\n",
       "      <td>eleme</td>\n",
       "      <td>2019-10-10</td>\n",
       "      <td>2000507076</td>\n",
       "      <td>五角场店</td>\n",
       "      <td>319.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>4636</td>\n",
       "      <td>蛙小辣火锅杯（总账号）</td>\n",
       "      <td>上海</td>\n",
       "      <td>eleme</td>\n",
       "      <td>2019-10-11</td>\n",
       "      <td>2000555792</td>\n",
       "      <td>虹口足球场店</td>\n",
       "      <td>754.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>4636</td>\n",
       "      <td>蛙小辣火锅杯（总账号）</td>\n",
       "      <td>上海</td>\n",
       "      <td>eleme</td>\n",
       "      <td>2019-10-13</td>\n",
       "      <td>2000507076</td>\n",
       "      <td>五角场店</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2050.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>4636</td>\n",
       "      <td>蛙小辣火锅杯（总账号）</td>\n",
       "      <td>上海</td>\n",
       "      <td>eleme</td>\n",
       "      <td>2019-10-16</td>\n",
       "      <td>2000555792</td>\n",
       "      <td>虹口足球场店</td>\n",
       "      <td>1293.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>609.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1416.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>4636</td>\n",
       "      <td>蛙小辣火锅杯（总账号）</td>\n",
       "      <td>上海</td>\n",
       "      <td>eleme</td>\n",
       "      <td>2019-10-18</td>\n",
       "      <td>2000555792</td>\n",
       "      <td>虹口足球场店</td>\n",
       "      <td>2485.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>1116.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3305.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2380</th>\n",
       "      <td>6108</td>\n",
       "      <td>拌客（武宁路店）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2020-08-13</td>\n",
       "      <td>9698624</td>\n",
       "      <td>拌客干拌麻辣烫(武宁路店)</td>\n",
       "      <td>3877.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1396.0</td>\n",
       "      <td>2087.0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2381</th>\n",
       "      <td>6108</td>\n",
       "      <td>拌客（武宁路店）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>9698624</td>\n",
       "      <td>拌客干拌麻辣烫(武宁路店)</td>\n",
       "      <td>4386.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1598.0</td>\n",
       "      <td>2343.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5588.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2382</th>\n",
       "      <td>6108</td>\n",
       "      <td>拌客（武宁路店）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2020-08-12</td>\n",
       "      <td>9698624</td>\n",
       "      <td>拌客干拌麻辣烫(武宁路店)</td>\n",
       "      <td>4628.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1738.0</td>\n",
       "      <td>2416.0</td>\n",
       "      <td>430.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7411.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2383</th>\n",
       "      <td>6108</td>\n",
       "      <td>拌客（武宁路店）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2020-08-19</td>\n",
       "      <td>9698624</td>\n",
       "      <td>拌客干拌麻辣烫(武宁路店)</td>\n",
       "      <td>5410.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1737.0</td>\n",
       "      <td>3122.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7234.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2384</th>\n",
       "      <td>6108</td>\n",
       "      <td>拌客（武宁路店）</td>\n",
       "      <td>上海</td>\n",
       "      <td>meituan</td>\n",
       "      <td>2020-08-18</td>\n",
       "      <td>9698624</td>\n",
       "      <td>拌客干拌麻辣烫(武宁路店)</td>\n",
       "      <td>5350.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>2906.0</td>\n",
       "      <td>477.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6005.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>501.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>436 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      品牌ID         品牌名称  城市       平台         日期        门店ID           门店名称  \\\n",
       "251   4636  蛙小辣火锅杯（总账号）  上海    eleme 2019-10-10  2000507076           五角场店   \n",
       "258   4636  蛙小辣火锅杯（总账号）  上海    eleme 2019-10-11  2000555792         虹口足球场店   \n",
       "275   4636  蛙小辣火锅杯（总账号）  上海    eleme 2019-10-13  2000507076           五角场店   \n",
       "298   4636  蛙小辣火锅杯（总账号）  上海    eleme 2019-10-16  2000555792         虹口足球场店   \n",
       "314   4636  蛙小辣火锅杯（总账号）  上海    eleme 2019-10-18  2000555792         虹口足球场店   \n",
       "...    ...          ...  ..      ...        ...         ...            ...   \n",
       "2380  6108     拌客（武宁路店）  上海  meituan 2020-08-13     9698624  拌客干拌麻辣烫(武宁路店)   \n",
       "2381  6108     拌客（武宁路店）  上海  meituan 2020-08-17     9698624  拌客干拌麻辣烫(武宁路店)   \n",
       "2382  6108     拌客（武宁路店）  上海  meituan 2020-08-12     9698624  拌客干拌麻辣烫(武宁路店)   \n",
       "2383  6108     拌客（武宁路店）  上海  meituan 2020-08-19     9698624  拌客干拌麻辣烫(武宁路店)   \n",
       "2384  6108     拌客（武宁路店）  上海  meituan 2020-08-18     9698624  拌客干拌麻辣烫(武宁路店)   \n",
       "\n",
       "         GMV   下单人数    商家实收    商户补贴   平台补贴  无效订单数    曝光人数  有效订单数   进店人数  \n",
       "251    319.0    7.0   159.0   135.0   17.0    1.0   255.0    7.0   56.0  \n",
       "258    754.0   13.0   303.0   371.0   54.0    1.0   829.0   13.0   80.0  \n",
       "275   1077.0   20.0   462.0   499.0  121.0    1.0  2050.0   20.0  223.0  \n",
       "298   1293.0   19.0   549.0   609.0   80.0    1.0  1416.0   19.0  160.0  \n",
       "314   2485.0   37.0  1102.0  1116.0  178.0    1.0  3305.0   39.0  315.0  \n",
       "...      ...    ...     ...     ...    ...    ...     ...    ...    ...  \n",
       "2380  3877.0   79.0  1396.0  2087.0  314.0    3.0  6012.0   79.0  452.0  \n",
       "2381  4386.0   86.0  1598.0  2343.0  397.0    3.0  5588.0   87.0  411.0  \n",
       "2382  4628.0   99.0  1738.0  2416.0  430.0    3.0  7411.0   97.0  528.0  \n",
       "2383  5410.0  113.0  1737.0  3122.0  166.0    4.0  7234.0  113.0  551.0  \n",
       "2384  5350.0  104.0  1913.0  2906.0  477.0    5.0  6005.0  104.0  501.0  \n",
       "\n",
       "[436 rows x 16 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5dc94f53",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('result_1.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00ed6d2a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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